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Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications. 

With the rising need for on-body biometric sensing, the development of wearable electrophysiological sensors has been faster than ever. Surface electrodes placed on the skin need to be robust in order to measure biopotentials from the body reliably and comfortable for extended wearability. The electrical stability of nonpolarizable silver/silver chloride (Ag/AgCl) and its low-cost, commercial production have made these electrodes ubiquitous health sensors in the clinical environment, where wet gels and long wires are accommodated by patient immobility. However, smaller, dry electrodes with wireless acquisition are essential for truly wearable, continuous health sensing. Currently, techniques for the robust fabrication of custom Ag/AgCl electrodes are lacking. Here, we present three methods for the fabrication of Ag/AgCl electrodes: oxidizing Ag in a chlorine solution, electroplating Ag, and curing Ag/AgCl ink. Each of these methods is then used to create three different electrode shapes for wearable application. Bench-top and on-body evaluation of the electrode techniques was achieved by electrochemical impedance spectroscopy (EIS), calculation of variance in electrocardiogram (ECG) measurements, and analysis of auditory steady-state response (ASSR) measurement. Microstructures produced on the electrode by each fabrication technique were also investigated with scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). The custom Ag/AgCl electrodes were found to be efficient in comparison with standard, commercial Ag/AgCl wet electrodes across all three of our presented techniques, with Ag/AgCl ink shown to be the better out of the three in bench-top and biometric recordings. 

The objective of this study is to develop and use a virtual reality game as a tool to assess the effects of realistic stress on the behavioral and physiological responses of participants. The game is based on a popular Steam game called Keep Talking Nobody Explodes, where the players collaborate to defuse a bomb. Varying levels of difficulties in solving a puzzle and time pressures will result in different stress levels that can be measured in terms of errors, response times, and other physiological measurements. The game was developed using 3D programming tools including Blender and a virtual reality development kit (VRTK). To measure response times accurately, we added LSL (Lab Stream Layer) Markers to collect and synchronize physiological signals, behavioral data, and the timing of game events. We recorded Electrocardiogram (ECG) data during gameplay to assess heart rate and heart-rate variability (HRV) that have been shown as reliable indicators of stress. Our empirical results showed that heart rate increased significantly while HRV reduced significantly when the participants under high stress, which are consistent with the prior mainstream stress research. This VR game framework is publicly available in GitHub and allows researchers to measure and synchronize other physiological signals such as electroencephalogram, electromyogram, and pupillometry. 

A visual stimulator plays an important role in a steady-state visual evoked potential (SSVEP)-based braincomputer interface (BCI). In conventional BCI studies, SSVEPs have been elicited by either a single stimulus whose flickering frequency varies across trials or multiple stimuli flickering at different frequencies simultaneously. It has been implicitly assumed that the SSVEPs generated by the single- and multiple-target stimulation methods are comparable. However, no study has directly compared their efficacy in eliciting SSVEPs. This study, therefore, performed a quantitative comparison of signal-to-noise ratio (SNR) and classification accuracy using 4-class SSVEPs generated by these two methods. The classification accuracy was estimated by three commonly-used target identification algorithms including calibration-free canonical correlation analysis (CCA)-based method and template-based methods with CCA- and task-related component analysis (TRCA)-based spatial filters. The results showed that the single-target stimulation method led to significantly higher SNR and classification accuracy than its multi-target counterpart. 

Abstract Objective . This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) by leveraging cross-domain data transferring. Approach . We enhanced the state-of-the-art template-based SSVEP decoding through incorporating a least-squares transformation (LST)-based transfer learning to leverage calibration data across multiple domains (sessions, subjects, and electroencephalogram montages). Main results . Study results verified the efficacy of LST in obviating the variability of SSVEPs when transferring existing data across domains. Furthermore, the LST-based method achieved significantly higher SSVEP-decoding accuracy than the standard task-related component analysis (TRCA)-based method and the non-LST naive transfer-learning method. Significance . This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances. The proposed framework significantly improved the SSVEP decoding accuracy over the standard TRCA approach when calibration data are limited. Its performance in calibration reduction could facilitate plug-and-play SSVEP-based BCIs and further practical applications.